Modern human populations have experienced changes in diet and activity levels, leading to an increase in metabolic disorders. The IIS pathway is known to influence many aspects of growth, metabolism, and longevity but little is known about the genetic basis of standing variation in the pathway. The goal of this project is to examine the genetic basis of natural genetic variation in the insulin/insulin-like signaling (IIS) pathway. The project will utilize a newly developed resource for the genetic analysis of complex traits, the Drosophila Synthetic Population Resource (DSPR). Gene expression levels for every gene in the IIS pathway will be measured in three different nutritional conditions and QTL influencing these gene expression levels will be mapped. The project will identify the source of natural genetic variation in IIS and determine if genetic variants co-localize with the genes encoding major proteins in the pathway. Genes influencing gene expression will be identified in multiple nutritional environments, making it possible to determine if the same or different variants control the IIS pathway in different environments. In addition, the project will address whether the same master genes control several components of the IIS pathway. The project will also answer broad questions about the nature of the interactions between components of the IIS pathway using systems biology approaches. By using the DSPR, this project will be able to map QTL influencing the IIS pathway to 1-2 centiMorgans, estimate both the effect and frequency of those QTL and identify potentially causative sites. The IIS pathway is highly conserved between Drosophila melanogaster and humans and therefore, identifying the genes controlling IIS signaling in Drosophila may directly identify targets for pharmacological intervention, particularly if master genes are identified. Ultimately, this project will characteize the genetic basis of a central endocrine pathway that has direct implications for our understanding of metabolism and the internal allocation of resources.